1. Technical Field
Embodiments of present invention relate to automatic face recognition. In particular, example embodiments of the present invention relate to methods for automatic face recognition that employ appearance-based techniques.
2. Related Art
Face recognition systems generally include a database, known as a gallery, consisting of facial images from a set of known subjects of interest. The objective of a face recognition system is to determine the identity of an unknown input facial image, known as a probe. In other words, the task of a face recognition system is to determine the gallery subject to which the probe image belongs.
Among various face recognition methodologies, appearance-based approaches, which treat the two-dimensional facial images as a holistic pattern, are among the most promising solutions and have dominated the face recognition literature in the recent decade. In appearance-based approaches, each face pattern is represented by a column vector consisting of pixel intensity values in the image. Face recognition is therefore viewed as a multivariate statistical pattern recognition problem.
A major challenge to this approach is the high dimensionality of the sample space combined with a limited number of face samples available for training, also known as the “small sample size” problem. The small sample size problem makes many learning algorithms mathematically intractable or empirically unstable. In particular, if only one image sample per subject is available, which is a commonly encountered situation in realistic application scenarios, the small sample size problem becomes even more challenging.